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CellSage, a patented simulation software tool, excels in predicting battery aging and capacity loss across a variety of applications and chemistries. By simulating major degradation mechanisms such as loss of lithium inventory (LLI) and loss of active material (LAM), CellSage aids battery developers and researchers in optimizing design and life cycles, thereby lowering development costs. This software utilizes Sigmoidal Rate Expression (SRE) frameworks for accurate and high-fidelity modeling, which includes over 20 metrics covering capacity fade, internal resistance growth, and power fade, among others. Its sophisticated capabilities allow developers to precisely gauge performance under realistic operational conditions, providing invaluable insights into battery longevity. CellSage's innovative New Chemistry Import feature allows for the integration of emerging cell chemistries with minimal preliminary data. By reducing laboratory testing requirements, this tool accelerates R&D processes, enabling quicker market introduction of improved battery technologies.
The Advanced Electrolyte Model (AEM), exclusively available through Ridgetop Group and developed by Idaho National Laboratory, serves as a transformative molecular simulation tool for optimizing electrolyte chemistry. This software acts as a virtual laboratory, furnishing researchers with the capability to delve deeply into genome-level electrolyte properties. With each simulation run, it produces over 100 property metrics, thereby granting unprecedented insights into electrolyte behavior. AEM is equipped with a robust database containing over 60 solvents and 40+ salts, facilitating the exploration of complex, multi-solvent electrolyte combinations. This feature is especially beneficial for those engaged in developing electric vehicles and grid-scale storage solutions. Built on the scientific foundation of the Nonprimitive, Nonrestricted Associated Mean Spherical Approximation, along with an ion-solvation equation of state, AEM stands out for its precision in transport and thermodynamic predictions. The tool has undergone extensive validation across numerous conditions, achieving a deviation of 5-10% from laboratory data, thus proving its accuracy. By eliminating the guesswork from electrolyte qualification, AEM significantly reduces R&D costs and time, expediting breakthroughs in the clean energy sector.
Sentinel Motion is a pioneering Condition-Based Maintenance (CBM) and Prognostic Health Management (PHM) platform tailored for Industrial IoT, offering comprehensive asset health insights. Originally developed for aerospace gearboxes, this advanced system is now adept at monitoring the health of critical mechanical systems across diverse sectors, such as rail and defense. With the Sentinel Motion Development Kit (SMDK), users gain a customizable platform integrating various sensor systems, local analysis, and secure data transfer capabilities, enabling early detection of mechanical anomalies and trends. This modular setup includes RotoSense IoT sensors, a Sentinel Gateway for data transfer, and the Sentinel MotionView software for analytical visualization. The Sentinel Motion system stands out for its ability to streamline predictive maintenance, minimizing unplanned downtime. It adheres to industry-standard PHM protocols and is adaptable to third-party software through its open API. This makes Sentinel Motion a versatile and scalable tool for implementing smart diagnostic strategies across various industrial applications.
The Adaptive Remaining Useful Life Estimator (ARULE) is a sophisticated predictive analytics platform engineered to deliver real-time system health estimations. Using innovative methods like adaptive Kalman filtering, ARULE provides precise predictions of remaining useful life (RUL), state of health (SoH), and prognostic horizon (PH) across various domains, such as aerospace and defense. The system's predictive capabilities are made possible by its ability to transform raw data into actionable insights. ARULE is designed to operate efficiently under diverse conditions, accommodating both low-noise and high-noise environments with minimal data points. It aligns with the standards of IEEE 1856-2017 regarding Prognostics and Health Management (PHM), making it versatile across multiple environments, whether integrated into software, firmware, or hardware. The platform's open API and simplified modeling flexibility allows for rapid integration into contemporary health-aware systems. As a result, ARULE empowers industries to transition from reactive maintenance approaches to more predictive and preemptive strategies, reducing costs and enhancing system reliability.
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